Estimating Maize Canopy Nitrogen and Chlorophyll Content Using CNN‐GRU‐CBAM and Hyperspectral Imagery

ABSTRACT Rapid, noninvasive quantification of canopy nitrogen (N) and chlorophyll (Chl) content is critical for precision nitrogen management in maize cultivation. Although near‐infrared spectroscopy (near‐infrared spectroscopy, NIRS) offers a viable approach for biochemical component analysis, conventional machine learning models often fail to capture the complex nonlinear relationships inherent in spectral data and lack interpretability, limiting their robustness for real‐time inversion tasks. To address these limitations, this study introduces a hybrid deep learning architecture combining convolutional neural networks (CNNs) and gated recurrent units (GRUs), augmented by a convolutional block attention module (CBAM), integrated with explainable artificial intelligence for accurate biochemical content inversion. Preprocessing of hyperspectral images from 200 maize canopy samples via sequential Savitzky–Golay smoothing (SG), standard normal variate (SG‐SNV), and SG transformations enhanced mean test set R 2 by 0.016 units. Subsequent dimensionality reduction via the successive projection algorithm (SPA) and competitive adaptive reweighting sampling (CARS) significantly reduced spectral features from 176 to 10 and 22 bands, respectively. The core predictive model synergistically combines CNNs and GRUs, augmented by a CBAM to enhance feature extraction and temporal dependency modeling. Comparative evaluation demonstrates the superior performance of CNN‐GRU‐CBAM over traditional machine learning and alternative deep learning models. For the test set, it achieved R 2 values of 0.934 (N) and 0.788 (Chl), with corresponding root mean square error (RMSE) values of 1.940 and 0.216. Model interpretability was rigorously validated using Shapley Additive Explanations (SHAP), identifying key spectral regions driving predictions. This work innovatively bridges high‐performance deep learning with explainable artificial intelligence, enabling precise, nondestructive estimation of maize foliar biochemical constituents. The framework provides a transferable approach for biochemical content inversion in diverse crops.